Knowledge of a state of a battery is important for a number of battery management applications. For example, state of the battery can include state of charge (SoC), which is defined as the percentage of available charge remaining in the battery. The SoC gives an indication when the battery should be recharged, which can enable battery management systems to improve the battery life by protecting the battery from over-discharge and over-charge events. Another indicator of the health of a battery is the state of power (SoP), which describes the maximum charging and discharging capabilities of the battery. For example, the estimation of the peak power capability of the battery is needed to determine the maximum available power for acceleration and regenerating braking of the electric vehicles, thus avoiding over-charging or over-discharging the battery. Accordingly, there is a need to estimate a state of a battery.
Rechargeable batteries store energy through a reversible chemical reaction. Conventionally, rechargeable batteries provide a lower cost of use and result in supporting Green initiatives toward impacting the environmental than non-rechargeable batteries. For example, Lithium-ion (Li-ion) rechargable batteries have been widely deployed as a major energy storage component in numerous applications including consumer electronics, residential rooftop solar photovoltaic systems, electric vehicles, smart grid systems, etc. At least some main advantages of Li-ion batteries over other types of batteries with different chemistries are low self-discharge rate, high cell voltage, high energy density, lightweight, long lifetime, and low maintenance.
However, a Li-ion battery and other types of batteries include a chemical energy storage source, and this chemical energy cannot be directly accessed. Conventional state of the battery estimation techniques are usually classified into model-based and data-driven based methods. Model-based methods exploit models capturing battery's chemical and/or physical processes. Data-driven methods use training data to map the measurements of physical quantities of the battery to corresponding values of its state. However, the processes in the battery are very complex and can vary over time, which reduces the accuracy of the conventional state estimation methods. Accordingly, there is still a need for a system and a method for estimating the state of a rechargeable battery.